Behavioral science initiatives to reduce individuals' climate footprint

We will differentially expose participants to messaging about their consumption through a mobile app.

2024-05-13

2025-02-01

Consumption
Political behavior and attitudes

Consumption will be measured using bank transaction data and register data. We will measure consumption in terms of carbon footprint and expenditures/purchases in various categories.
Political behavior and attitudes will be measured using register and survey data. We will link register data on voting participation as a binary measure of political behavior. We will also provide subjects an opportunity to donate to an environmental cause and to sign an environmental petition as measures of political behavior. In terms of the survey, we will ask participants about their political preferences, their voting choice in the EU parliament elections, their opinion on environmental policies, and their opinion on the role of government and individual responsibility for confronting societal challenges. As an additional measure of political behavior, we are planning follow-ups with subjects to learn whether they have taken part in a climate demonstration.

Green investments
Carbon literacy
Perceived behavioral plasticity and intention to reduce carbon footprint

Investments (e.g., stock portfolio, pension fund choices) will be observed through register data. We will construct a measure of how green the participant's investments are.
After exposure to treatment, we will gather survey data from participants about their knowledge of the carbon intensiveness of common activities. This outcome will help shed light on users’ learning and the informational value of the treatment.
As part of the treatment, users will be presented with a set of “action cards” that describe measures they can take to reduce their carbon footprints. We will observe and analyze their choice of which action cards they choose to save, which indicates the actions they would like to undertake, in whole or in part, in the next 6 months. We can subset our analysis of consumption outcomes based on the action cards saved by users, which may shed light on mechanisms for the treatment effect.

We will differentially expose participants to messaging about their consumption through a mobile app. Participants will be randomized into two versions of the mobile app: (i) a carbon footprint calculator and (ii) a financial management calculator. Both versions of the app will have a similar look and feel, but the treatment (carbon footprint calculator) will expose participants to personal responsibility messaging for addressing climate change.

Not available

randomization done in office by a computer

individual

No

3000 individuals

3000 individuals

1500 individuals control app; 1500 individuals treatment app

With an intended sample size of 3000 individuals and 80% power, we will have an MDE for consumption (carbon footprint) on the order of 0.04 log points. In pilot data, the mean for ln(carbon footprint + 1) is 4.14 and the standard deviation for this outcome is 1.37 log points, so our MDE is roughly 3% of a standard deviation.